Efficient and Accurate Candidate Generation for Grasp Pose

Por um escritor misterioso

Descrição

Recently, a number of grasp detection methods have been proposed that can be used to localize robotic grasp configurations directly from sensor data without estimating object pose. The underlying idea is to treat grasp perception analogously to object detection in computer vision. These methods take as input a noisy and partially occluded RGBD image or point cloud and produce as output pose estimates of viable grasps, without assuming a known CAD model of the object. Although these methods generalize grasp knowledge to new objects well, they have not yet been demonstrated to be reliable enough for wide use. Many grasp detection methods achieve grasp success rates (grasp successes as a fraction of the total number of grasp attempts) between 75% and 95% for novel objects presented in isolation or in light clutter. Not only are these success rates too low for practical grasping applications, but the light clutter scenarios that are evaluated often do not reflect the realities of real world grasping. This paper proposes a number of innovations that together result in a significant improvement in grasp detection performance. The specific improvement in performance due to each of our contributions is quantitatively measured either in simulation or on robotic hardware. Ultimately, we report a series of robotic experiments that average a 93% end-to-end grasp success rate for novel objects presented in dense clutter.
Efficient and Accurate Candidate Generation for Grasp Pose
Evaluation on the GraspNet dataset
Efficient and Accurate Candidate Generation for Grasp Pose
Frontiers Robotics Dexterous Grasping: The Methods Based on
Efficient and Accurate Candidate Generation for Grasp Pose
Publications The Helping Hands Lab
Efficient and Accurate Candidate Generation for Grasp Pose
Grasp detection via visual rotation object detection and point
Efficient and Accurate Candidate Generation for Grasp Pose
Robotics, Free Full-Text
Efficient and Accurate Candidate Generation for Grasp Pose
A grasps-generation-and-selection convolutional neural network for
Efficient and Accurate Candidate Generation for Grasp Pose
Dex-Net 2.0 pipeline for training dataset generation. (Left) The
Efficient and Accurate Candidate Generation for Grasp Pose
3D Grasp Pose Generation from 2D Anchors and Local Surface
Efficient and Accurate Candidate Generation for Grasp Pose
Evaluation of the grasp candidates on the XOZ plane (a) initial
Efficient and Accurate Candidate Generation for Grasp Pose
Graphical model for robust parallel-jaw grasping of objects on a
Efficient and Accurate Candidate Generation for Grasp Pose
PEGG-Net: Background Agnostic Pixel-Wise Efficient Grasp
Efficient and Accurate Candidate Generation for Grasp Pose
Machines, Free Full-Text
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